Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa
Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly su...
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MDPI AG
2018-07-01
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Series: | International Journal of Environmental Research and Public Health |
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Online Access: | http://www.mdpi.com/1660-4601/15/7/1452 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Apolline Saucy Martin Röösli Nino Künzli Ming-Yi Tsai Chloé Sieber Toyib Olaniyan Roslynn Baatjies Mohamed Jeebhay Mark Davey Benjamin Flückiger Rajen N. Naidoo Mohammed Aqiel Dalvie Mahnaz Badpa Kees de Hoogh |
spellingShingle |
Apolline Saucy Martin Röösli Nino Künzli Ming-Yi Tsai Chloé Sieber Toyib Olaniyan Roslynn Baatjies Mohamed Jeebhay Mark Davey Benjamin Flückiger Rajen N. Naidoo Mohammed Aqiel Dalvie Mahnaz Badpa Kees de Hoogh Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa International Journal of Environmental Research and Public Health air pollution informal settlements modelling environmental exposure exposure assessment land use regression nitrogen dioxide particulate matter South Africa Western Cape |
author_facet |
Apolline Saucy Martin Röösli Nino Künzli Ming-Yi Tsai Chloé Sieber Toyib Olaniyan Roslynn Baatjies Mohamed Jeebhay Mark Davey Benjamin Flückiger Rajen N. Naidoo Mohammed Aqiel Dalvie Mahnaz Badpa Kees de Hoogh |
author_sort |
Apolline Saucy |
title |
Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa |
title_short |
Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa |
title_full |
Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa |
title_fullStr |
Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa |
title_full_unstemmed |
Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa |
title_sort |
land use regression modelling of outdoor no2 and pm2.5 concentrations in three low income areas in the western cape province, south africa |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1660-4601 |
publishDate |
2018-07-01 |
description |
Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO2 and PM2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015–2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO2 and PM2.5 were 22.1 μg/m3 and 10.2 μg/m3, respectively. The NO2 models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R2). The PM2.5 annual models had lower explanatory power (R2 = 0.36, 0.29, and 0.29). The best predictors for NO2 were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM2.5, together with population density. This study demonstrates that land-use-regression modelling for NO2 can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO2 and PM2.5 seasonal exposure estimates and maps for further health studies. |
topic |
air pollution informal settlements modelling environmental exposure exposure assessment land use regression nitrogen dioxide particulate matter South Africa Western Cape |
url |
http://www.mdpi.com/1660-4601/15/7/1452 |
work_keys_str_mv |
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doaj-ff3eb7a5ba994f898c58d272b6844bcd2020-11-25T01:04:38ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012018-07-01157145210.3390/ijerph15071452ijerph15071452Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South AfricaApolline Saucy0Martin Röösli1Nino Künzli2Ming-Yi Tsai3Chloé Sieber4Toyib Olaniyan5Roslynn Baatjies6Mohamed Jeebhay7Mark Davey8Benjamin Flückiger9Rajen N. Naidoo10Mohammed Aqiel Dalvie11Mahnaz Badpa12Kees de Hoogh13Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, SwitzerlandDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, SwitzerlandDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, SwitzerlandEnvironmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195 USADepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, SwitzerlandCentre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South AfricaCentre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South AfricaCentre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South AfricaDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, SwitzerlandDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, SwitzerlandDiscipline of Occupational and Environmental Health, School of Nursing and Public Health, University of KwaZulu-Natal, 4041 Durban, South AfricaCentre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South AfricaDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, SwitzerlandDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, SwitzerlandAir pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO2 and PM2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015–2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO2 and PM2.5 were 22.1 μg/m3 and 10.2 μg/m3, respectively. The NO2 models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R2). The PM2.5 annual models had lower explanatory power (R2 = 0.36, 0.29, and 0.29). The best predictors for NO2 were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM2.5, together with population density. This study demonstrates that land-use-regression modelling for NO2 can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO2 and PM2.5 seasonal exposure estimates and maps for further health studies.http://www.mdpi.com/1660-4601/15/7/1452air pollutioninformal settlementsmodellingenvironmental exposureexposure assessmentland use regressionnitrogen dioxideparticulate matterSouth AfricaWestern Cape |